library(tidyverse)
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## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
# install.packages("vtable")
library(vtable)
## Loading required package: kableExtra
##
## Attaching package: 'kableExtra'
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## group_rows
# install.packages("Hmisc")
library("Hmisc")
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
##
## Attaching package: 'Hmisc'
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##
## src, summarize
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## format.pval, units
# install.packages("corrplot")
library(corrplot)
## corrplot 0.92 loaded
library(naniar)
library(corrplot)
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
library(htmltools)
df <- read_csv("../../data/final/merged_data.csv")
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## iso = col_character(),
## country_name = col_character(),
## hdi_value = col_character()
## )
## ℹ Use `spec()` for the full column specifications.
adb <- read_csv("../../data/final/adb-members.csv")
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## country_name = col_character(),
## iso = col_character(),
## region = col_character(),
## donor = col_double(),
## sids = col_double(),
## ldc = col_double()
## )
output.fig.dir <- "../../output/figures"
output.tab.dir <- "../../output/tables"
SAVE.RESULTS = TRUE
df <- df %>%
rename(migrant_stock=ims_both_sex,
refugee_stock=estimated_refugee_stock_incl_asylum_seekers_both_sexes,
disaster_displacement=disaster_stock_displacementr_raw,
conflict_displacement=conflict_stock_displacement_raw,
climate_change=CCH,
air_quality=AIR,
rule_of_law=`value.Rule of Law: Estimate`,
gov_effectiveness=`value.Government Effectiveness: Estimate`,
corruption_control=`value.Control of Corruption: Estimate`,
state_legit=`P1: State Legitimacy`,
cpa_d_12=D12,
cpa_d_avg=D_avg,
gdp=`GDP per capita (constant 2015 US$)`,
gini=`value.Gini index (World Bank estimate)`) %>%
mutate(conflict_displacement=conflict_displacement/10000,
disaster_displacement=disaster_displacement/10000,
migrant_stock=migrant_stock/10000,
refugee_stock=refugee_stock/10000,
gdp=gdp/1000,
state_legit=10-state_legit,
hdi_value=as.numeric(hdi_value))
## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
col_names <- c('migrant_stock'='Migrant Stock (10,000s)',
'refugee_stock'='Refugee Stock (10,000s)',
'disaster_displacement'='Internal Displacement Due to Disasters (10,000s)',
'conflict_displacement'='Internal Displacement Due to Conflict (10,000s)',
'climate_change'='Climate Change',
'air_quality'='Air Quality',
'rule_of_law'='Rule of Law',
'gov_effectiveness'='Government Effectiveness',
'corruption_control'='Control of Corruption',
'state_legit'='State Legitimacy',
'cpa_d_12'='CPA: D-12',
'cpa_d_avg'='CPA: Cluster D Average',
'gdp'='GDP Per Capita (1,000s)',
'hdi_value'="HDI",
'gini'="Gini Index")
generic.cols <- c('iso', 'year', 'country_name', 'region', 'donor', 'sids', 'ldc')
outcome.cols <- c('state_legit', 'cpa_d_avg', 'cpa_d_12')
keep <- df %>%
arrange(year, iso) %>%
select(names(col_names)) %>%
mutate(keep = if_any(everything(), ~ !is.na(.))) %>%
pull(keep)
df <- df %>%
arrange(year, iso) %>%
select(iso, year, names(col_names)) %>%
arrange(year) %>%
filter(keep) %>%
left_join(adb, by='iso')
for (reg in unique(df$region)) {
for (outcome in outcome.cols) {
y.lim <- 5
if (outcome == 'state_legit') {y.lim <- 10}
plt <- df %>%
filter(region==reg) %>%
select('iso', 'year', outcome) %>%
drop_na() %>%
ggplot(aes_string(x='year', outcome)) +
geom_line(aes(color=iso)) +
geom_point(aes(color=iso)) +
labs(title=reg, subtitle=paste(col_names[outcome], 'across years')) +
ylim(0, y.lim) +
theme_classic()
print(plt)
if (SAVE.RESULTS) {
ggsave(paste(output.fig.dir, '/year_X_', outcome, '_', tolower(str_replace(reg, " " ,"-")), '.png', sep=''))
}
}
}
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(outcome)` instead of `outcome` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
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for (reg in unique(df$region)) {
for (outcome in outcome.cols) {
y.lim <- 5
if (outcome == 'state_legit') {y.lim <- 10}
plt <- df %>%
filter(region==reg & donor==0) %>%
select('iso', 'year', outcome) %>%
drop_na() %>%
ggplot(aes_string(x='year', outcome)) +
geom_line(aes(color=iso)) +
geom_point(aes(color=iso)) +
labs(title=reg, subtitle=paste(col_names[outcome], 'across years')) +
ylim(0, y.lim) +
theme_classic()
print(plt)
if (SAVE.RESULTS) {
ggsave(paste(output.fig.dir, '/year_X_', outcome, '_', tolower(str_replace(reg, " " ,"-")), '_recipients', '.png', sep=''))
}
}
}
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for (status in c('Donors', 'Recipients')) {
for (outcome in outcome.cols) {
y.lim <- 5
if (outcome == 'state_legit') {y.lim <- 10}
plt <- df %>%
mutate(donor=ifelse(donor, 'Donors', 'Recipients')) %>%
filter(donor==status) %>%
select('iso', 'year', outcome) %>%
drop_na() %>%
ggplot(aes_string(x='year', outcome)) +
geom_line(aes(color=iso)) +
geom_point(aes(color=iso)) +
labs(title=status, subtitle=paste(col_names[outcome], 'across years')) +
ylim(0, y.lim) +
theme_classic()
print(plt)
if (SAVE.RESULTS) {
ggsave(paste(output.fig.dir, '/year_X_', outcome, '_', tolower(status), '.png', sep=''))
}
}
}
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## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(generic.cols)` instead of `generic.cols` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(col)` instead of `col` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
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## Warning in max(sdf$year): no non-missing arguments to max; returning -Inf
## Warning in min(sdf$year): no non-missing arguments to min; returning Inf
## Warning in max(sdf$year): no non-missing arguments to max; returning -Inf
## Warning in min(sdf$year): no non-missing arguments to min; returning Inf
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## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
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## increasing max.overlaps
## Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
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https://www.princeton.edu/~otorres/Panel101R.pdf panel_model <- plm(state_leg ~ climate, data = data_set, indec = c(“iso”, “year”), model = “within”) Maya Van Nuys (she/her) to Everyone (1:31 PM) panel data code (updated): panel_model <- plm(state_leg ~ climate, data = data_set, index = c(“iso”, “year”), model = “within”) library(AER) library(plm)
panel_model <- plm(state_leg ~ climate, data = data_set, index = c(“iso”, “year”), model = “within”, effect = “twoways”)
library(AER)
## Loading required package: car
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
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## some
## Loading required package: lmtest
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: sandwich
library(plm)
##
## Attaching package: 'plm'
## The following objects are masked from 'package:dplyr':
##
## between, lag, lead
# plm(state_legit ~ air_quality + climate_change + hdi_value + gdp, data = df, index = c("iso", "year"), model = "within")
climate.model <- plm(state_legit ~ air_quality + climate_change + hdi_value + gdp, data = df, index = c("iso", "year"), model = "within", effect = "twoways")
migration.model <- plm(state_legit ~ migrant_stock + refugee_stock + conflict_displacement + disaster_displacement + gdp, data = df, index = c("iso", "year"), model = "within", effect = "twoways")
governance.model <- plm(state_legit ~ rule_of_law + gov_effectiveness + corruption_control + gdp, data = df, index = c("iso", "year"), model = "within", effect = "twoways")
# full.model <- plm(state_legit ~ rule_of_law + gov_effectiveness + corruption_control + migrant_stock + refugee_stock + conflict_displacement + disaster_displacement + climate_change + air_quality + hdi_value + gdp, data = df, index = c("iso", "year"), model = "within", effect = "twoways")
stargazer(climate.model, migration.model, governance.model, type='text')
##
## ===================================================================================
## Dependent variable:
## -------------------------------------------------------------
## state_legit
## (1) (2) (3)
## -----------------------------------------------------------------------------------
## air_quality 0.007
## (0.005)
##
## climate_change -0.004
## (0.004)
##
## hdi_value 6.820
## (6.205)
##
## migrant_stock -0.001
## (0.004)
##
## refugee_stock 0.003
## (0.008)
##
## conflict_displacement -0.003
## (0.002)
##
## disaster_displacement 0.008*
## (0.004)
##
## rule_of_law -0.098
## (0.228)
##
## gov_effectiveness -0.438**
## (0.191)
##
## corruption_control 0.869***
## (0.179)
##
## gdp -0.025 -0.134 -0.046**
## (0.048) (0.095) (0.022)
##
## -----------------------------------------------------------------------------------
## Observations 178 116 562
## R2 0.025 0.109 0.063
## Adjusted R2 -0.317 -0.423 -0.039
## F Statistic 0.848 (df = 4; 131) 1.766 (df = 5; 72) 8.476*** (df = 4; 506)
## ===================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
if (SAVE.RESULTS) {
stargazer(climate.model, migration.model, governance.model, type='html', out=paste(output.tab.dir, '/state_legit_reg_fe.html', sep=''))
}
##
## <table style="text-align:center"><tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="3"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="3" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td colspan="3">state_legit</td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">air_quality</td><td>0.007</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.005)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">climate_change</td><td>-0.004</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.004)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">hdi_value</td><td>6.820</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(6.205)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">migrant_stock</td><td></td><td>-0.001</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.004)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">refugee_stock</td><td></td><td>0.003</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.008)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">conflict_displacement</td><td></td><td>-0.003</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">disaster_displacement</td><td></td><td>0.008<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.004)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">rule_of_law</td><td></td><td></td><td>-0.098</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.228)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">gov_effectiveness</td><td></td><td></td><td>-0.438<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.191)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">corruption_control</td><td></td><td></td><td>0.869<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.179)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">gdp</td><td>-0.025</td><td>-0.134</td><td>-0.046<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.048)</td><td>(0.095)</td><td>(0.022)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>178</td><td>116</td><td>562</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.025</td><td>0.109</td><td>0.063</td></tr>
## <tr><td style="text-align:left">Adjusted R<sup>2</sup></td><td>-0.317</td><td>-0.423</td><td>-0.039</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>0.848 (df = 4; 131)</td><td>1.766 (df = 5; 72)</td><td>8.476<sup>***</sup> (df = 4; 506)</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="3" style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>